Camera Reprojection for Faces

ABSTRACT

In one embodiment, one or more computing systems may receive an image of a portion of a face of a first user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the first user. The one or more computing systems may identify one or more facial features captured in the image and determine a camera pose relative to the 3D facial model based on the identified one or more facial features in the image and predetermined feature locations on the 3D facial model. The one or more computing systems may determine a mapping relationship between the image and the 3D facial model by projecting the image of the portion of the face of the first user onto the 3D facial model from the camera pose and cause an output image of a facial representation of the first user to be rendered.

TECHNICAL FIELD

This disclosure generally relates to controls and interfaces for userinteractions and experiences in a virtual reality environment.

BACKGROUND

Virtual reality is a computer-generated simulation of an environment(e.g., a 3D environment) that users can interact with in a seeminglyreal or physical way. A virtual reality system, which may be a singledevice or a group of devices, may generate this simulation for displayto a user, for example, on a virtual reality headset or some otherdisplay device. The simulation may include images, sounds, hapticfeedback, and/or other sensations to imitate a real or imaginaryenvironment. As virtual reality becomes more and more prominent, itsrange of useful applications is rapidly broadening. The most commonapplications of virtual reality involve games or other interactivecontent, but other applications such as the viewing of visual mediaitems (e.g., photos, videos) for entertainment or training purposes areclose behind. The feasibility of using virtual reality to simulatereal-life conversations and other user interactions is also beingexplored.

SUMMARY OF PARTICULAR EMBODIMENTS

Disclosed herein are a variety of different ways of rendering andinteracting with a virtual (or augmented) reality environment. Anartificial reality system may render an artificial environment, whichmay include a virtual space that is rendered for display to one or moreusers. For instance, a virtual reality environment may be rendered, oran augmented reality environment may be rendered. The users may view andinteract within this virtual space and the broader virtual environmentthrough any suitable means. One goal of the disclosed methods is toreproject a facial representation of a user within an artificial realityenvironment. In particular embodiments one or more computing systems mayprovide a method of reprojecting a facial representation of a userwithin an artificial reality environment. To start, the one or morecomputing systems may receive one or more captured images of a portionof a face of a user. The captured images may be taken by an inside-outcamera coupled to an artificial reality system worn by the user. The oneor more computing systems may access a three-dimensional (3D) facialmodel representative of the face of the user. The one or more computingsystems may identify facial features captured in the one or more images.The one or more computing systems may determine a camera pose relativeto the 3D facial model based on the identified facial features andpredetermined feature locations on the 3D facial model for each cameraassociated with the one or more captured images. After determining thecamera pose(s), the one or more computing systems may determine amapping relationship between the captured image(s) and the 3D facialmodel. To determine the mapping relationship, the one or more computingsystems may project the captured image(s) of portions of the face of theuser onto the 3D facial model from the determined camera pose. The oneor more computing systems may cause an output image of a facialrepresentation of a user to be rendered by using the 3D facial model andthe mapping relationship between the captured image(s) and the 3D facialmodel. For instance, the one or more computing systems may sendinstructions to an artificial reality system of another user to renderthe facial representation of the user in an artificial realityenvironment.

Embodiments of the invention may include or be implemented inconjunction with an artificial reality system. Artificial reality is aform of reality that has been adjusted in some manner beforepresentation to a user, which may include, e.g., a virtual reality (VR),an augmented reality (AR), a mixed reality (MR), a hybrid reality, orsome combination and/or derivatives thereof. Artificial reality contentmay include completely generated content or generated content combinedwith captured content (e.g., real-world photographs). The artificialreality content may include video, audio, haptic feedback, or somecombination thereof, and any of which may be presented in a singlechannel or in multiple channels (such as stereo video that produces athree-dimensional effect to the viewer). Additionally, in someembodiments, artificial reality may be associated with applications,products, accessories, services, or some combination thereof, that are,e.g., used to create content in an artificial reality and/or used in(e.g., perform activities in) an artificial reality. The artificialreality system that provides the artificial reality content may beimplemented on various platforms, including a head-mounted display (HMD)connected to a host computer system, a standalone HMD, a mobile deviceor computing system, or any other hardware platform capable of providingartificial reality content to one or more viewers.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed above.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates an example artificial reality system.

FIG. 2 illustrates another example artificial reality system.

FIG. 3 illustrates example camera positions of an artificial realitysystem in relation to a user.

FIG. 4 illustrates example images captured by cameras of an artificialreality system.

FIGS. 5A-5B illustrate example areas of reprojection from a camera of anartificial reality system.

FIG. 6 illustrates example rendered two-dimensional images of athree-dimensional model from different perspectives.

FIGS. 7A-7B illustrates an example process of reprojecting a facialrepresentation onto a three-dimensional model.

FIG. 8 illustrates an example computing system in an artificial realityenvironment.

FIG. 9 illustrates an example method for reprojecting a facialrepresentation onto a facial model.

FIG. 10 illustrates an example network environment associated with avirtual reality system.

FIG. 11 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

As more people adopt artificial reality systems, more people will beginto use artificial reality systems for a variety of reasons. One use casemay generally include face-to-face interactions. These face-to-faceinteractions may be within an augmented reality environment, virtualreality environment, and/or a combination of the two environments. Forinstance, avatars or visual representations (photorealistic or not) maybe used to represent each user in a face-to-face interaction, such as avirtual meeting. Each user may be presented an environment where theycan see other users in the face-to-face interaction. However, currentlythese artificial environments between users may not be able to capturethe facial expressions of a user that people are accustomed to seeing ina typical face-to-face interaction. As such, a facial reprojectionwithin a virtual reality environment may improve upon user experienceswhile interacting with other users in an artificial reality environment.However, that may not be a simple problem to solve when a user iswearing a virtual reality headset that occludes part of the user's faceand images of the user's face are captured from extreme viewpoints. Assuch, facial models may be used to in conjunction with machine learningmodels to improve upon camera reprojection of faces within a virtualreality environment.

In particular embodiments, an artificial reality system may have one ormore cameras that are capturing a user's facial features. As an exampleand not by way of limitation, a virtual reality headset may havemultiple inside-out cameras that are capturing a user's facial features.These inside-out cameras may be used to capture the user's facialfeatures (e.g., a portion of the user's mouth, a portion of the eyes,etc.). Landmarks in the images may be used to morph a facial model tocustomize it for the user, and the images may be used to create atexture for the corresponding portion of the facial model. As an exampleand not by way of limitation, there can be an average facial model thatis representative of a person's face. When the user puts on the headset,the headset's cameras may capture images of the user's mouth region.Landmarks of the captured mouth region may be detected and matchedagainst the facial model to determine the poses (position andorientation) of the cameras relative to the facial model. The capturedimages can be reprojected from the cameras onto the facial model todetermine the mapping between the images and the geometry of the facialmodel (e.g., the images can be used as textures for the facial model).With the facial model, the static texture of the user's overall face,and the dynamic texture generated based on the captured images, theartificial reality system could render images of an avatar orphotorealistic representation from desired viewpoints to represent theuser's face in a virtual reality environment.

In particular embodiments, a machine-learning model may be used tosynthesize an image of a portion of the user's face. While some camerasof an artificial reality headset may be able to capture portions of theface clearly, other cameras of the artificial reality headset may not beable to accurately capture the portion of the face clearly. This isespecially true for facial features that have complex geometric details(e.g., eyes) and because of the viewpoints of the cameras may differsignificantly from a desired rendering viewpoint. Using the cameras ofan artificial reality headset, images may be captured from each of thesecameras and inputted into a machine-learning model to generate an imagerepresenting a facial portion. As an example and not by way oflimitation, one camera may capture the user's eye from one angle andanother camera may capture the user's eye from a different angle. Theseseparate images may be combined to generate a synthesized image of whatit would look like from a frontal view of the eye. The machine-learningmodel may be trained using ground truth images taken from a desiredfrontal viewpoint. The synthesized image may be reprojected onto a meshor three-dimensional (3D) facial model from the pose of the camera usedby the ground truth images (e.g., if the ground truth images were takenby a camera that is directly in front of the user's face at 6″, centeredbetween the eyes, then the synthesized image would be reprojected fromsuch a camera instead of the real cameras used to capture the images).

In particular embodiments, one or more computing systems may perform theprocessing as described herein. The one or more computing system may beembodied as a social-networking system, a third-party system, anartificial reality system, another computing system, and/or acombination of these computing systems. The one or more computingsystems may be coupled to a plurality of artificial reality systems. Theplurality of artificial reality systems may be embodied as an augmentedreality headset, a virtual reality headset, or a hybrid reality headset,and the like. In particular embodiments, the one or more computingsystems may receive input data from the plurality of artificial realitysystems. In particular embodiments, the input data may comprise imagescaptured from one or more cameras coupled to an artificial realitysystem.

In particular embodiments, the one or more computing systems may receivean image of a portion of a face of a user. In particular embodiments,the one or more computing systems may receive an image of a portion of aface of a user from an artificial reality system. The image may becaptured by a camera coupled to the artificial reality system. As anexample and not by way of limitation, the image may be captured by aninside-out camera of an artificial reality headset worn by a user, wherethe image may capture a portion of the user's mouth. The image maycorrespond to other portions of the face of the user based on the camerapose of the camera. In particular embodiments, the one or more computingsystems may receive a plurality of images corresponding to one or moreportions of the user's face from multiple cameras. As an example and notby way of limitation, the one or more computing systems may receive twoimages corresponding to the user's mouth and one image corresponding tothe user's right eye from several inside-out cameras coupled to anartificial reality headset. In particular embodiments, multiple camerasmay be coupled to an artificial reality system at various camera posesrelative to the artificial reality system. As an example and not by wayof limitation, a camera may be placed on the top portion of anartificial reality system and another camera may be placed on the bottomportion of the artificial reality system. Although this disclosuredescribes receiving an image of a portion of a face of a user in aparticular manner, this disclosure contemplates receiving an image of aportion of a face of a user in any suitable manner.

In particular embodiments, the one or more computing systems may accessa three-dimensional (3D) facial model representative of a face of auser. In particular embodiments, the one or more computing systems mayretrieve a 3D facial model from storage or request a 3D facial modelfrom another computing system. In particular embodiments, the one ormore computing systems may select a 3D facial model based on the user.As an example and not by way of limitation, the one or more computingsystems may identify characteristics of the user, such as requestinguser input, and selecting a 3D facial model that best represents theuser based on the user input. For instance, if a user inputs that he issix feet tall, African American, and of a slim build, then the one ormore computing system may retrieve a 3D facial model that mostaccurately represents the user based on the user inputs. The one or morecomputing systems may use other factors to access an appropriate 3Dfacial model representative of the face of the user. In particularembodiments, the 3D facial model may be a predetermined 3D facial modelrepresentative of a plurality of faces of a plurality of users. As anexample and not by way of limitation, a general 3D facial model may beused for all users, only males, or only females. In particularembodiments, the 3D facial model may represent a 3D space that a head ofa user would occupy within an artificial reality environment. As anexample and not by way of limitation, the 3D facial model may be a meshthat textures can be applied to represent a face of a user. Althoughthis disclosure describes accessing a 3D facial model representative ofa face of a user in a particular manner, this disclosure contemplatesaccessing a 3D facial model representative of a face of a user in anysuitable manner.

In particular embodiments, the one or more computing systems mayidentify one or more facial features captured in the image. Inparticular embodiments, the one or more computing systems may perform aprocess of facial feature detection to identify facial features capturedin the image. As an example and not by way of limitation, the one ormore computing systems may use a machine-learning model to identify acheek and a nose captured within an image. In particular embodiments, aparticular camera of artificial reality system may be designated tocapture only certain features of a user's face. As an example and not byway of limitation, an inside-out camera coupled to the bottom of avirtual reality headset would only capture facial features located onthe bottom of the user's face, so this particular inside-out camera mayonly identify a mouth or a chin. The particular inside-out camera maynot be able to identify an eye for instance. This may reduce the numberof features the particular inside-out camera is attempting to identifybased on its position on the virtual reality headset. In particularembodiments, the one or more computing systems may morph a predetermined3D facial model representative of a plurality of faces of a plurality ofusers based on at least the identified one or more facial features inthe image. As an example and not by way of limitation, the one or morecomputing systems may identify based on the identified facial featuresthat the face of the user is slightly narrower than the predetermined 3Dfacial model representative of a plurality of faces of a plurality ofusers and morph the 3D facial model accordingly so that the 3D facialmodel is representative of a facial model that pertains to the user. Inparticular embodiments, the one or more computing system may request animage of the face of the user. In particular embodiments, the one ormore computing systems may identify privacy settings of the user todetermine whether the one or more computing systems may access an imageof the face of the user. As an example and not by way of limitation, ifthe user gives permission to the one or more computing systems, the oneor more computing systems may access photos associated with the userthrough an online social network to retrieve an image representative ofthe face of the user. This retrieved image(s) may be analyzed todetermine a static texture that is representative of the face of theuser. As an example and not by way of limitation, the one or moreretrieved images may be analyzed to determine where facial features aregenerally located on the face of the user. The analyzed images may becompared to the 3D facial model, and the 3D facial model may be morphedbased on the analyzed images. Although this disclosure describesidentifying one or more facial features captured in the image in aparticular manner, this disclosure contemplates identifying one or morefacial features captured in the image in any suitable manner.

In particular embodiments, the one or more computing systems maydetermine a camera pose relative to the 3D facial model. In particularembodiments, the one or more computing systems may use the identifiedone or more facial features in a captured image and predeterminedfeature locations on a 3D facial model in order to determine a camerapose. In particular embodiments, the one or more computing systems maycompare locations of identified facial features captured in the image topredetermined feature locations. As an example and not by way oflimitation, the one or more computing systems may identify a location ofa chin and a location of a mouth of a user. The identified locations ofthe chin and mouth with respect to the camera and each other may be usedto compare to predetermined feature locations of the 3D facial model.For instance, the chin and mouth may be located in a particular locationin a captured image for a given camera pose. Given the identifiedlocations of these facial features in the captured image, the one ormore computing systems may determine where the camera pose is based onhow the identified locations would compare to the predetermined featurelocations. As an example and not by way of limitation, if a capturedimage contains a chin 30 pixels from the bottom of the captured imageand 50 pixels from the left of the captured image and a mouth 60 pixelsfrom the top of the captured image and 40 pixels from the right of thecaptured image, then the one or more computing systems may determinethat the camera that captured the image may be at a particular camerapose with respect to the face of the user and the 3D facial model. Giventhat the heads of users will vary from person to person, the comparisonof the identified facial features to the 3D facial model may allow theone or more computing systems to determine approximately the camera poseof the camera capturing the image relative to the 3D facial model.Although this disclosure describes determining a camera pose relative tothe 3D facial model in a particular manner, this disclosure contemplatesdetermining a camera pose relative to the 3D facial model in anysuitable manner.

In particular embodiments, the one or more computing systems maydetermine a mapping relationship between a captured image and a 3Dfacial model. In particular embodiments, the one or more computingsystems may determine the mapping relationship between an image and a 3Dfacial model by projecting the image of a portion of a face of a useronto the 3D facial model from the determined camera pose. As an exampleand not by way of limitation, the one or more computing systems maycapture a portion of a face of a user, such as the mouth of a user.Since the camera pose of the camera that captured the image (e.g, aninside-out camera capturing the image of the mouth of the user) is notreadily known, the one or more computing systems may determine thecamera pose as described herein. By using the camera pose, the one ormore computing systems may project the captured image onto the 3D facialmodel in order to determine a mapping relationship. For instance, whichpixel of the captured image of the mouth of the user belongs at aparticular location of the 3D facial model. The 3D facial model is usedas a mesh to project the image of the mouth of the user onto 3D facialmodel. In particular embodiments, the mapping relationship may be atexture image of the portion of the face of the user. Although thisdisclosure describes determining a mapping relationship between acaptured image and a 3D facial model in a particular manner, thisdisclosure contemplates determining a mapping relationship between acaptured image and a 3D facial model in any suitable manner.

In particular embodiments, the computing system may cause an outputimage of a facial representation of the user to be rendered. Inparticular embodiments, the computing system may use at least the 3Dfacial model and the mapping relationship between a captured image andthe 3D facial model to cause the rendering of an output image of afacial representation of the user. In particular embodiments, the one ormore computing systems may send instructions to another computing systemto render the output image of a facial representation of the user. As anexample and not by way of limitation, the one or more computing systemsmay send instructions to an artificial reality system of a first user torender an output image of a facial representation of a second user. Theone or more computing systems may initially receive one or more capturedimages of a face of the second user from the artificial reality systemof the second user. The one or more computing systems may determine amapping relationship between the image and a 3D facial modelrepresentative of the face of the second user as described herein. Theone or more computing systems may send the mapping relationship betweenthe one or more captured images and the 3D facial model to theartificial reality system of the first user. The artificial realitysystem of the first user may render the facial representation of thesecond user based on the received mapping relationship between the oneor more captured images and the 3D facial model. In particularembodiments, a rendering package may be sent to an artificial realitysystem to render a facial representation of a user. The renderingpackage may contain the 3D facial model used and the mappingrelationship between a captured image and the 3D facial model used. Asan example and not by way of limitation, if the rendering package isused to render a facial representation of a second user, then therendering package may include a 3D facial model of the second user and atexture image of the face of the second user. While the general processof reprojecting a face of a user is generally discussed in relation to aportion of the face of the user, the one or more computing systems mayreceive a plurality of images corresponding to various portions of theface of the user in order to create a texture image of the whole face ofthe user. As an example and not by way of limitation, the one or morecomputing systems may receive images of the eyes of a user, the mouth ofthe user, the nose of the user, the cheeks of the user, the forehead ofthe user, the chin of the user, and the like. Each of these images maybe used to identify various facial features of the face of the user anddetermine mapping relationships corresponding to the image and a 3Dfacial model of the user. The mapping relationship may be used toproject the full face of the user onto a 3D facial model. In particularembodiments, the output image of the facial representation of the usermay be photorealistic so that it appears a user is looking at the faceof another user. In particular embodiments, the output image of thefacial representation of the user may be an avatar that is mappedaccording to the mapping relationship and the 3D facial model. As anexample and not by way of limitation, the mapping relationship may beused to determine the current state of a face of a user (e.g., whetherthe user is smiling, frowning, moving their face in a certain way, andthe like) and reproject that onto the avatar to represent the facialrepresentation of the user. In particular embodiments, the rendering ofan output image may be based on a viewpoint of the user of theartificial reality system that is rendering the facial representationwith respect to the user whose face is being rendered. As an example andnot by way of limitation, the facial representation of a user mayconsider in which direction another user is looking at another user inan artificial reality system. When a user is looking at another userdirectly, the rendered output image may be a facial representation ofthe user that is facing the other user. However, if the user is lookingat the other user indirectly (e.g., from the side), the rendered outputimage may be a facial representation of the user from a side profile.Although this disclosure describes causing an output image of a facialrepresentation of the user to be rendered in a particular manner, thisdisclosure contemplates causing an output image of a facialrepresentation of the user to be rendered in any suitable manner.

In particular embodiments, the one or more computing systems maygenerate a synthesized image corresponding to a portion of the face of auser. In particular embodiments, the one or more computing systems mayreceive a plurality of images corresponding to a portion of the face ofthe user. As an example and not by way of limitation, the one or morecomputing systems may receive multiple images corresponding to the eyesof a user. In particular embodiments, the angles of cameras capturingimages of a portion of the face of a user may be at extreme angles inrelation to the face of the user. As an example and not by way oflimitation, the camera may be located close to the face of a user. Giventhe extreme angle, a direct reprojection of the captured image mapped toa 3D facial model may not be an accurate facial representation and mayintroduce artifacts during the reprojection process. As such, inparticular embodiments, a machine-learning model may be used to generatea synthesized image corresponding to one or more portions of the face ofthe user. As an example and not by way of limitation, given that theeyes portion of the face of a user is typically occluded by anartificial reality system (e.g., a virtual reality headset), multipleimages of the eyes of the user at different angles may be compiled togenerate a synthesized image of the eyes of the user. In particularembodiments, the machine-learning model may be trained based on groundtruth images captured by a camera at a predetermined camera pose inrelation to a face of a user. As an example and not by way oflimitation, during a training process of a machine-learning model tosynthesize multiple images to generate a texture or determine a mappingrelationship between a synthesized image and a 3D facial model, imagesof the eyes of a user may be captured by an artificial reality system(e.g., captured by inside-out cameras of the artificial reality system).In a separate process, a camera at a predetermined camera pose maycapture images of the eyes of the user in an unobstructed manner (e.g.,the user is not wearing an artificial reality system), which wouldrepresent a ground truth image for the machine-learning model to compareto a rendered image. The machine-learning model may compare a renderedattempt (i.e., the synthesized image) of a portion of a face of a userbased on the captured images to the ground truth image. By training themachine-learning model, the one or more computing systems may use themachine-learning model to synthesize multiple images to generate asynthesized image corresponding to a portion of a face of a userassociated with the multiple images. When reprojecting a textureassociated with the synthesized image, the texture may be projected ontoa 3D facial model at the same camera pose of the camera that capturedthe ground truth image. As an example and not by way of limitation, theone or more computing systems may cause an output of a facialrepresentation of a user to be rendered by at least projecting thesynthesized image onto the 3D facial model from a predetermined camerapose. Although this disclosure describes generating a synthesized imagecorresponding to a portion of the face of a user in a particular manner,this disclosure contemplates generating a synthesized imagecorresponding to a portion of the face of a user in any suitable manner.

In particular embodiments, the one or more computing systems may blend atexture image with a predetermined texture. In particular embodiments,the texture image may be the mapping relationship between a capturedimage of a portion of a face of a user and a 3D facial model of theuser. The texture image may be represented by an image to project onto a3D facial model to represent the portion of a face of the user thatcorresponds to the texture image. As an example and not by way oflimitation, if a captured image is of a right side of a mouth of a user,the texture image may represent the right side of a mouth of a user toproject onto a 3D facial model. The projected texture image would be apart of a process of reprojecting the entire face of the user. Forinstance, the one or more computing systems would receive a capturedimage of the left side of the mouth of the user, the right cheek of theuser, the left cheek of the user, and so on to determine a mappingrelationship between the captured images to the 3D facial model toproject each of the captured images onto the 3D facial model. Inparticular embodiments, a predetermined texture may be based on accessedimages that the one or more computing systems may access based onprivacy settings of the user. The predetermined texture may represent astatic texture of the face of the user. As an example and not by way oflimitation, the one or more computing systems may retrieve one or moreimages of the face of the user and generate and/or determine a statictexture of the face of the user. The predetermined texture may representwhat the face of the user typically looks like, such as where the facialfeatures are located and the like. In particular embodiments, the one ormore computing systems may blend the texture image with thepredetermined texture. As an example and not by way of limitation, givena predetermined texture, the one or more computing systems may cause theprojection of the texture image onto a 3D facial model with thepredetermined texture to accurately reflect the face of a user at acurrent time. For instance, if a user is currently smiling, the textureimage may correspond to the mouth of the user and the eyes of the userand be blended with a predetermined texture of the face of the user. Theblended texture image with the predetermined image may be used to rendera facial representation of the user that is smiling in an artificialreality environment. In particular embodiments, during the renderingprocess, the one or more computing systems may sample a point on apredetermined texture corresponding to a facial representation of a userto identify a first color associated with the point. As an example andnot by way of limitation, the one or more computing systems may sample apixel corresponding to the nose of the user to identify the color of thepixel corresponding to the nose. In particular embodiments, the one ormore computing systems may sample another point on a texture image thatcorresponds to the point on the predetermined texture to identifyanother color associated with the point. As an example and not by way oflimitation, the one or more computing systems may sample the same pointon the texture image corresponding to the predetermined texture. Forinstance, if a point corresponding to a pixel of a nose is being sampledon the predetermined texture, the point corresponding to the same pixelof the nose is being sampled on the texture image. The colors of thesamples corresponding to the predetermined texture and the texture imagemay be different. In particular embodiments, the one or more computingsystems may blend the color corresponding to the sample from thepredetermined texture with the color corresponding to the sample fromthe texture image to generate a final color associated with the locationcorresponding to the sample. As an example and not by way of limitation,if the sample of the predetermined texture is brown and the sample ofthe of the texture image is dark brown, the one or more computingsystems may blend the colors and generate a final color that is betweenbrown and dark brown.

FIG. 1 illustrates an example artificial reality system 100. Inparticular embodiments, the artificial reality system 100 may comprise aheadset 104, a controller 106, and a computing system 108. A user 102may wear the headset 104 that may display visual artificial realitycontent to the user 102. The headset 104 may include an audio devicethat may provide audio artificial reality content to the user 102. As anexample and not by way of limitation, the headset 104 may display visualartificial content and audio artificial reality content corresponding toa virtual meeting. The headset 104 may include one or more cameras whichcan capture images and videos of environments. The headset 104 mayinclude a plurality of sensors to determine a head pose of the user 102.The headset 104 may include a microphone to receive audio input from theuser 102. The headset 104 may be referred as a head-mounted display(HMD). The controller 106 may comprise a trackpad and one or morebuttons. The controller 106 may receive inputs from the user 102 andrelay the inputs to the computing system 108. The controller 106 mayalso provide haptic feedback to the user 102. The computing system 108may be connected to the headset 104 and the controller 106 throughcables or wireless connections. The computing system 108 may control theheadset 104 and the controller 106 to provide the artificial realitycontent to and receive inputs from the user 102. The computing system108 may be a standalone host computer system, an on-board computersystem integrated with the headset 104, a mobile device, or any otherhardware platform capable of providing artificial reality content to andreceiving inputs from the user 102.

FIG. 2 illustrates an example artificial reality system 200. Theartificial reality system 200 may be worn by a user to display anartificial reality environment to the user. The artificial realitysystem may comprise displays 202 a, 202 b to display content to theuser. As an example and not by way of limitation, the artificial realitysystem may generate an artificial reality environment of an office spaceand render an avatar of a user comprising a facial representation of theuser. In particular embodiments, the artificial reality system maycomprise a plurality of cameras 204, 206 a, 206 b. As an example and notby way of limitation, the artificial reality system 200 may comprise aplurality of inside-out cameras coupled to the artificial reality system200 to capture images of various portions of the face of the user. As anexample and not by way of limitation, the camera 204 may capture the topportion of the face of the user and part of the eyes of the user. Inparticular embodiments, the artificial reality system 200 may send thecaptured images from the plurality of cameras 204, 206 a, 206 b toanother computing system. The computing system may process the capturedimages to send data (e.g., a mapping relationship, 3D facial model, andthe like) to another artificial reality system 200 to render a facialrepresentation of the user. While a certain number of components ofartificial reality system 200 is shown, the artificial reality system200 may comprise more or less components and/or in differentconfigurations. As an example and not by way of limitation, theartificial reality system 200 may comprise an additional camera and theconfiguration of all of the cameras 204, 206 a, 206 b may change toaccommodate the extra camera. For instance, there may be two additionalcameras, and cameras 206 a, 206 b may be positioned closer to thedisplays 202 a, 202 b.

FIG. 3 illustrates an environment 300 representative of a plurality ofcameras (e.g., such as cameras 204, 206 a, 206 b of artificial realitysystem 200) in relation to the face of a user. In particularembodiments, several angles 302 a-302 c display the distance between themultiple cameras 304, 306 a, 306 b in relation to the face of a user. Inparticular embodiments, the cameras 304, 306 a, 306 b may be coupled toan artificial reality system that is not shown. In particularembodiments, the various angles 302 a-302 c display the cameras 304, 306a, 306 b at specific distances from the face of the user at specificcamera poses. In particular embodiments, the several angles 302 a-302 cportray approximate distances between the cameras 304, 306 a, 306 b inrelation to the face of the user. While the cameras 304, 306 a, 306 bare shown to be configured in a certain way, the cameras 304, 306 a, 306b may be reconfigured in a different way. As an example and not by wayof limitation, cameras 306 a, 306 b may be positioned lower in relationto the face of the user and closer to the face of the user. As anotherexample and not by way of limitation, there may be additional camerasand cameras 304, 306 a, 306 b may be repositioned to accommodate theadditional cameras.

FIG. 4 illustrates example images captured by cameras of an artificialreality system. As an example and not by way of limitation, the exampleimages may be captured by cameras 304, 306 a, 306 b of FIG. 3. Inparticular embodiments, camera 304 may capture image 402 of a portion ofa face of the user. In particular embodiments, the image 402 maycorrespond to the top portion of the face of the user. In particularembodiments, camera 306 a may capture image 404 of a portion of a faceof the user. In particular embodiments, the image 404 may correspond toa right eye of the user. In particular embodiments, camera 306 b maycapture image 406 of a portion of a face of the user. In particularembodiments, the image 406 may correspond to a left eye of the user.While the images 402, 404, 406 are shown to correspond to particularportions of a face of a user, the images 402, 404, 406 may correspond todifferent portions of the face of the user based on the correspondingcamera associated with the image 402, 404, 406. In particularembodiments, there may any number of images that correspond to a portionof a face of the user. As an example and not by way of limitation, theremay be three cameras positioned on the right side of the face of theuser to capture three images that correspond to the right side of theface of the user.

FIGS. 5A-5C illustrate example areas of reprojection from a camera of anartificial reality system. In particular embodiments, an environment 500representative of a plurality of cameras (e.g., such as cameras 204, 206a, 206 b of artificial reality system 200) in relation to the face of auser is shown. Referring to FIG. 5A, example areas of reprojection fromcamera (e.g., camera 204) are shown within an environment 500 arepresentative of a plurality of cameras in relation to the face of auser. In particular embodiments, several angles 502 a-502 c of the areasof reprojection are shown. In particular embodiments, at a given angle502 a, the area of reprojection 504 of a camera (e.g., camera 204) mayinclude an area between the cheeks of the user. In particularembodiments, at a given angle 502 b, the area of reprojection 506 of acamera (e.g., camera 204) may include an area slightly encompassing thenose of the user. In particular embodiments, at a given angle 502 c, thearea of reprojection 508 of a camera (e.g., camera 204) may include anarea from the forehead of the user to the tip of the nose of the user.In particular embodiments, the areas of reprojection 504, 506, 508 mayall correspond to the same area of reprojection except that the area ofreprojection is shown at the different angles 502 a-502 c. In particularembodiments, after one or more computing systems determines a mappingrelationship between the captured image, such as a mapping relationshipbetween image 402 to a 3D facial model or mesh as shown in FIG. 5A, thenthe one or more computing systems may cause an artificial reality systemto render a facial representation of a face of a user by projecting thetexture image onto the area of reprojection 504, 506, 508 correspondingto the camera that captured the image. Although example areas ofreprojection 504, 506, 508 are shown for the particular camera, thecamera may have different areas of reprojection. As an example and notby way of limitation, the camera may have a wider field of view andinclude a larger area of reprojection. As another example and not by wayof limitation, if there are additional cameras, then the area ofreprojection may be smaller.

Referring to FIG. 5B, example areas of reprojection from cameras (e.g.,cameras 206 a, 206 b) are shown within an environment 500 brepresentative of a plurality of cameras in relation to the face of auser. In particular embodiments, several angles 502 a-502 c of the areasof reprojection are shown. In particular embodiments, at a given angle502 a, the areas of reprojection 510 a, 510 b of cameras (e.g., camera206 a, 206 b) may include areas corresponding to both eyes of the user.In particular embodiments, at a given angle 502 b, the areas ofreprojection 512 a, 512 b of cameras (e.g., camera 206 a, 206 b) mayinclude areas between the nose of the user and the eyes of the user. Inparticular embodiments, at a given angle 502 c, the areas ofreprojection 514 a, 514 b (only area of reprojection 514 a is shown) ofcameras (e.g., camera 206 a, 206 b) may include areas from the top ofthe eyes to the bottom of the eyes of the user. In particularembodiments, the areas of reprojection 510 a, 510 b, 512 a, 512 b, 514a, 514 b may all correspond to the same area of reprojection except thatthe area of reprojection is shown at the different angles 502 a-502 c.In particular embodiments, after one or more computing systemsdetermines a mapping relationship between the captured images, such as amapping relationship between images 404, 406 to a 3D facial model ormesh as shown in FIG. 5B, then the one or more computing systems maycause an artificial reality system to render a facial representation ofa face of a user by projecting the texture images onto the areas ofreprojection 510 a, 510 b, 512 a, 512 b, 514 a, 514 b corresponding tothe camera that captured the image. Although example areas ofreprojection 510 a, 510 b, 512 a, 512 b, 514 a, 514 b are shown for theparticular camera, the camera may have different areas of reprojection.As an example and not by way of limitation, the camera may have a widerfield of view and include a larger area of reprojection. As anotherexample and not by way of limitation, if there are additional cameras,then the area of reprojection may be smaller.

FIG. 6 illustrates example rendered two-dimensional (2D) images of a 3Dmodel from different perspectives. In particular embodiments, image 602may represent a real-world view of a face of a user. In particularembodiments, image 604 may represent an example 2D rendered image basedon the face shown in image 602 and using cameras with a narrow field ofview. As an example and not by way of limitation, multiple cameras maycapture images of various portions of the face of the user. Thesecaptured images may be used to determine a mapping relationship or atexture image shown in image 604. In particular embodiments, image 606may represent an example 2D rendered image based on the face shown inimage 602 and using cameras with a wider field of view. In particularembodiments, image 608 may represent another real-world view of a faceof a user. In particular embodiments, image 610 may represent an example2D rendered image based on the face shown in image 608 and using cameraswith a narrow field of view. In particular embodiments, image 612 mayrepresent an example 2D rendered image based on the face shown in image608 and using cameras with a wider field of view.

FIGS. 7A-7B illustrates an example process 700 of reprojecting a facialrepresentation onto a 3D model. Referring to FIG. 7A, in particularembodiments, the process 700 may begin with either a texture image 702or texture image 704 determined from captured images and a 3D facialmodel 710 as described herein. In particular embodiments, the textureimage 702 or 704 may be blended with a predetermined texture 706. Thepredetermined texture 706 may be generated based on images of a face ofa user as described herein. In particular embodiments, the blending ofthe texture image 702 or 704 with the predetermined texture 706 maygenerate a new texture 708. Referring to FIG. 7B, the process 700 maycontinue by projecting the new texture 708 onto the 3D facial model 710.In particular embodiments, the result of projecting the new texture 708onto the 3D facial model 710 may be the image 712 where a facialrepresentation of the user is shown with the new texture 708 projectedonto the 3D facial model 710. In particular embodiments, the blendbetween the texture image 702 or 704 with predetermined texture 706 isnoticeable. In particular embodiments, during the reprojection process700, the artificial reality system rendering the facial representationmay blend the texture image 702 or 704 with predetermined texture 706 insuch a way that removes the sharp contrast between the two textures.

FIG. 8 illustrates an example computing system 802 in an artificialreality environment 800. In particular embodiments, the computing system802 may be embodied as an augmented reality headset, virtual realityheadset, a server, a social-networking system, a third-party system, ora combination thereof. Although shown as an individual computing system802, the computing system 802 may be represented by one or morecomputing systems as described herein. In particular embodiments, thecomputing system 802 may interface one or more artificial realitysystems (e.g., an augmented reality headset, virtual reality, headset,and the like). In particular embodiments, the computing system 802 maycomprise an input module 804, a feature identifier module 806, a camerapose determination module 808, a mapping module 810, a synthesizermodule 812, a morphing module 814, and a reprojection module 816.

In particular embodiments, the input module 804 may interface one ormore artificial reality systems in an artificial reality environment 800to receive input data. In particular embodiments, the input data may beembodied as captured images from one or more artificial reality systems.As an example and not by way of limitation, an artificial reality systemmay capture images of portions of a face of a user from inside-outcameras and send the captured images to the computing system 802 asdescribed herein. The input module 804 may send the captured images tothe other modules of the computing system 802. As an example and not byway of limitation, the input module 804 may send the input data to thefeature identifier module 806, mapping module 810, and/or synthesizermodule 812.

In particular embodiments, the feature identifier module 806 mayidentify one or more facial features from the input data received fromthe input module 804. In particular embodiments, the feature identifiermodule 806 may perform a facial feature identification process. Inparticular embodiments, the feature identifier module 806 may use amachine-learning model to identify one or more facial features within acaptured image. In particular embodiments, the captured image may beassociated with a particular camera. As an example and not by way oflimitation, the captured image may be associated with (e.g., capturedby) the top camera coupled to an artificial reality system. The featureidentifier module 806 may use the information to reduce the number offacial features to identify. As an example and not by way of limitation,if the captured image came from the top camera, the feature identifiermodule 806 would attempt to identify facial features corresponding tothe eyes of a user, a nose of a user, and other facial features locatedon the top half of the face of the user. In particular embodiments, thefeature identifier module may identify a location associated with theidentified facial feature. In particular embodiments, the featureidentifier module 806 may send the results of the feature identificationprocess to other modules of the computing system 802. As an example andnot by way of limitation, the feature identifier module 806 may send theresults to the camera pose determination module 808 and/or morphingmodule 814.

In particular embodiments, the camera pose determination module 808 maydetermine the camera pose of the camera that captured the imageassociated with the identified facial features results sent by thefeature identifier module 806. In particular embodiments, the camerapose determination module 808 may access a 3D facial model of a user asdescribed herein. The camera pose determination module 808 may comparethe locations of the identified facial features of the captured image topredetermined feature locations of the 3D facial model in order todetermine the camera pose corresponding to the captured image. Inparticular embodiments, if the camera pose determination module 808received multiple facial feature results corresponding to multiplecameras, the camera pose determination module 808 may determine thecamera poses of each of the cameras that captured images sent to thecomputing system. After determining the camera pose(s), the camera posedetermination module 808 may send the determined camera pose(s)associated with one or more captured images to other modules of thecomputing system 802. As an example and not by way of limitation, thecamera pose determination module 808 may send one or more determinedcamera poses to a mapping module 810.

In particular embodiments, the mapping module 810 may determine amapping relationship between a captured image from an input module 804and a 3D facial model of a face of a user by projecting the capturedimage onto the 3D facial model from the determined camera posecorresponding to the captured image. In particular embodiments, themapping module 810 may project multiple captured images onto the 3Dfacial model to determine a mapping relationship between captured imagesand a 3D facial model. In particular embodiments, the mapping module 810may send the determined mapping relationship to other modules of thecomputing system 802. As an example and not by way of limitation, themapping module 810 may send the mapping relationship to a reprojectionmodule 816.

In particular embodiments, the synthesizer module 812 may receive inputdata from the input module 804 corresponding to multiple images of aportion of a face of a user. As an example and not by way of limitation,the synthesizer module 812 may receive multiple images of the eyes of auser. In particular embodiments, the synthesizer module 812 maysynthesize the multiple images into a synthesized image as describedherein. The synthesize module 812 may send the synthesized image to theother modules of the computing system 802. As an example and not by wayof limitation, the synthesizer module 812 may send the synthesized imageto the reprojection module 816 and/or to the mapping module 810. Inparticular embodiments, the mapping module 810 may determine a mappingrelationship between the synthesized image and the 3D facial model asdescribed herein.

In particular embodiments, the morphing module 814 may receiveidentified facial feature results from the feature identifier module806. In particular embodiments, the morphing module 814 may access a 3Dfacial model representative of a face of a user. In particularembodiments, the morphing module 814 may morph the 3D facial model asdescribed herein. As an example and not by way of limitation, themorphing module 814 may use the identified facial features compared tothe 3D facial model to determine whether the 3D facial model needs to bemorphed. For instance, if the morphing module 814 determines that noseof the user is 3 inches away from the chin and the 3D facial model has adistance of 2.7 inches between the nose and the chin, the morphingmodule 814 may morph the 3D facial model to change the distance betweenthe nose and chin of the 3D facial model to 3 inches. In particularembodiments, the morphing module 814 may send the results of a morphed3D facial model to the other modules of the computing system 802. As anexample and not by way of limitation, the morphing module 814 may sendthe morphed 3D facial model to the mapping module 810 and/or thereprojection module 816. In particular embodiments, the mapping module810 may use the morphed 3D facial model to determine a mappingrelationship between a captured image and the morphed 3D facial modelinstead of the original 3D facial model.

In particular embodiments, the reprojection module 816 may receive amapping relationship from the mapping module. In particular embodiments,the reprojection module 816 may interface one or more artificial realitysystems. In particular embodiments, the reprojection module 816 maygenerate instructions to cause an artificial reality system to render anoutput image of a facial representation of a user based on the mappingrelationship between a captured image and 3D facial model (morphed ornot morphed). In particular embodiments, the reprojection module 816 maygenerate a reprojection package to send to an artificial reality system,the reprojection package may include the mapping relationship, the 3Dfacial model of the user, and instructions to render the facialrepresentation of the user based on the mapping relationship and the 3Dfacial model.

FIG. 9 illustrates an example method 900 for reprojecting a facialrepresentation onto a facial model. The method 900 may begin at step910, where one or more computing systems may receive an image of aportion of a face of a first user. In particular embodiments, the imagemay be captured by a camera coupled to an artificial-reality headsetworn by the first user. At step 920, the one or more computing systemsmay access a three-dimensional (3D) facial model representative of theface of the first user. At step 930, the one or more computing systemsmay identify one or more facial features captured in the image. At step940, the one or more computing systems may determine a camera poserelative to the 3D facial model based on the identified one or morefacial features in the image and predetermined feature locations on the3D facial model. At step 950, the one or more computing systems maydetermine a mapping relationship between the image and the 3D facialmodel by projecting the image of the portion of the face of the firstuser onto the 3D facial model from the camera pose. At step 960, the oneor more computing systems may cause an output image of a facialrepresentation of the first user to be rendered using at least the 3Dfacial model and the mapping relationship between the image and the 3Dfacial model. Particular embodiments may repeat one or more steps of themethod of FIG. 9, where appropriate. Although this disclosure describesand illustrates particular steps of the method of FIG. 9 as occurring ina particular order, this disclosure contemplates any suitable steps ofthe method of FIG. 9 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method forreprojecting a facial representation onto a facial model, including theparticular steps of the method of FIG. 9, this disclosure contemplatesany suitable method of reprojecting a facial representation onto afacial model, including any suitable steps, which may include all, some,or none of the steps of the method of FIG. 9, where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method of FIG. 9, this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method of FIG. 9.

FIG. 10 illustrates an example network environment 1000 associated witha virtual reality system. Network environment 1000 includes a user 1001interacting with a client system 1030, a social-networking system 1060,and a third-party system 1070 connected to each other by a network 1010.Although FIG. 10 illustrates a particular arrangement of a user 1001, aclient system 1030, a social-networking system 1060, a third-partysystem 1070 and a network 1010, this disclosure contemplates anysuitable arrangement of a user 1001, a client system 1030, asocial-networking system 1060, a third-party system 1070, and a network1010. As an example and not by way of limitation, two or more of a user1001, a client system 1030, a social-networking system 1060, and athird-party system 1070 may be connected to each other directly,bypassing a network 1010. As another example, two or more of a clientsystem 1030, a social-networking system 1060, and a third-party system1070 may be physically or logically co-located with each other in wholeor in part. Moreover, although FIG. 10 illustrates a particular numberof users 1001, client systems 1030, social-networking systems 1060,third-party systems 1070, and networks 1010, this disclosurecontemplates any suitable number of client systems 1030,social-networking systems 1060, third-party systems 1070, and networks1010. As an example and not by way of limitation, network environment1000 may include multiple users 1001, client systems 1030,social-networking systems 1060, third-party systems 1070, and networks1010.

This disclosure contemplates any suitable network 1010. As an exampleand not by way of limitation, one or more portions of a network 1010 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 1010 may include one or more networks1010.

Links 1050 may connect a client system 1030, a social-networking system1060, and a third-party system 1070 to a communication network 1010 orto each other. This disclosure contemplates any suitable links 1050. Inparticular embodiments, one or more links 1050 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 1050 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 1050, or a combination of two or more such links1050. Links 1050 need not necessarily be the same throughout a networkenvironment 1000. One or more first links 1050 may differ in one or morerespects from one or more second links 1050.

In particular embodiments, a client system 1030 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 1030. As an example and not by way of limitation, a client system1030 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, virtual reality headset andcontrollers, other suitable electronic device, or any suitablecombination thereof. This disclosure contemplates any suitable clientsystems 1030. A client system 1030 may enable a network user at a clientsystem 1030 to access a network 1010. A client system 1030 may enableits user to communicate with other users at other client systems 1030. Aclient system 1030 may generate a virtual reality environment for a userto interact with content.

In particular embodiments, a client system 1030 may include a virtualreality (or augmented reality) headset 1032 and virtual reality inputdevice(s) 1034, such as a virtual reality controller. A user at a clientsystem 1030 may wear the virtual reality headset 1032 and use thevirtual reality input device(s) to interact with a virtual realityenvironment 1036 generated by the virtual reality headset 1032. Althoughnot shown, a client system 1030 may also include a separate processingcomputer and/or any other component of a virtual reality system. Avirtual reality headset 1032 may generate a virtual reality environment1036, which may include system content 1038 (including but not limitedto the operating system), such as software or firmware updates and alsoinclude third-party content 1040, such as content from applications ordynamically downloaded from the Internet (e.g., web page content). Avirtual reality headset 1032 may include sensor(s) 1042, such asaccelerometers, gyroscopes, magnetometers to generate sensor data thattracks the location of the headset device 1032. The headset 1032 mayalso include eye trackers for tracking the position of the user's eyesor their viewing directions. The client system may use data from thesensor(s) 1042 to determine velocity, orientation, and gravitationforces with respect to the headset. Virtual reality input device(s) 1034may include sensor(s) 1044, such as accelerometers, gyroscopes,magnetometers, and touch sensors to generate sensor data that tracks thelocation of the input device 1034 and the positions of the user'sfingers. The client system 1030 may make use of outside-in tracking, inwhich a tracking camera (not shown) is placed external to the virtualreality headset 1032 and within the line of sight of the virtual realityheadset 1032. In outside-in tracking, the tracking camera may track thelocation of the virtual reality headset 1032 (e.g., by tracking one ormore infrared LED markers on the virtual reality headset 1032).Alternatively or additionally, the client system 1030 may make use ofinside-out tracking, in which a tracking camera (not shown) may beplaced on or within the virtual reality headset 1032 itself. Ininside-out tracking, the tracking camera may capture images around it inthe real world and may use the changing perspectives of the real worldto determine its own position in space.

Third-party content 1040 may include a web browser and may have one ormore add-ons, plug-ins, or other extensions. A user at a client system1030 may enter a Uniform Resource Locator (URL) or other addressdirecting a web browser to a particular server (such as server 1062, ora server associated with a third-party system 1070), and the web browsermay generate a Hyper Text Transfer Protocol (HTTP) request andcommunicate the HTTP request to server. The server may accept the HTTPrequest and communicate to a client system 1030 one or more Hyper TextMarkup Language (HTML) files responsive to the HTTP request. The clientsystem 1030 may render a web interface (e.g. a webpage) based on theHTML files from the server for presentation to the user. This disclosurecontemplates any suitable source files. As an example and not by way oflimitation, a web interface may be rendered from HTML files, ExtensibleHyper Text Markup Language (XHTML) files, or Extensible Markup Language(XML) files, according to particular needs. Such interfaces may alsoexecute scripts, combinations of markup language and scripts, and thelike. Herein, reference to a web interface encompasses one or morecorresponding source files (which a browser may use to render the webinterface) and vice versa, where appropriate.

In particular embodiments, the social-networking system 1060 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 1060 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system1060 may be accessed by the other components of network environment 1000either directly or via a network 1010. As an example and not by way oflimitation, a client system 1030 may access the social-networking system1060 using a web browser of a third-party content 1040, or a nativeapplication associated with the social-networking system 1060 (e.g., amobile social-networking application, a messaging application, anothersuitable application, or any combination thereof) either directly or viaa network 1010. In particular embodiments, the social-networking system1060 may include one or more servers 1062. Each server 1062 may be aunitary server or a distributed server spanning multiple computers ormultiple datacenters. Servers 1062 may be of various types, such as, forexample and without limitation, web server, news server, mail server,message server, advertising server, file server, application server,exchange server, database server, proxy server, another server suitablefor performing functions or processes described herein, or anycombination thereof. In particular embodiments, each server 1062 mayinclude hardware, software, or embedded logic components or acombination of two or more such components for carrying out theappropriate functionalities implemented or supported by server 1062. Inparticular embodiments, the social-networking system 1060 may includeone or more data stores 1064. Data stores 1064 may be used to storevarious types of information. In particular embodiments, the informationstored in data stores 1064 may be organized according to specific datastructures. In particular embodiments, each data store 1064 may be arelational, columnar, correlation, or other suitable database. Althoughthis disclosure describes or illustrates particular types of databases,this disclosure contemplates any suitable types of databases. Particularembodiments may provide interfaces that enable a client system 1030, asocial-networking system 1060, or a third-party system 1070 to manage,retrieve, modify, add, or delete, the information stored in data store1064.

In particular embodiments, the social-networking system 1060 may storeone or more social graphs in one or more data stores 1064. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 1060may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 1060 and then add connections (e.g., relationships) to a numberof other users of the social-networking system 1060 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 1060 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 1060.

In particular embodiments, the social-networking system 1060 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 1060. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 1060may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 1060 or byan external system of a third-party system 1070, which is separate fromthe social-networking system 1060 and coupled to the social-networkingsystem 1060 via a network 1010.

In particular embodiments, the social-networking system 1060 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 1060 may enable users tointeract with each other as well as receive content from third-partysystems 1070 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 1070 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 1070 maybe operated by a different entity from an entity operating thesocial-networking system 1060. In particular embodiments, however, thesocial-networking system 1060 and third-party systems 1070 may operatein conjunction with each other to provide social-networking services tousers of the social-networking system 1060 or third-party systems 1070.In this sense, the social-networking system 1060 may provide a platform,or backbone, which other systems, such as third-party systems 1070, mayuse to provide social-networking services and functionality to usersacross the Internet.

In particular embodiments, a third-party system 1070 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 1030. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 1060 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 1060. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 1060. As an example and not by way oflimitation, a user communicates posts to the social-networking system1060 from a client system 1030. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 1060 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 1060 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 1060 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 1060 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 1060 may include one or more user-profilestores for storing user profiles. A user profile may include, forexample, biographic information, demographic information, behavioralinformation, social information, or other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, interests, affinities, or location. Interest informationmay include interests related to one or more categories. Categories maybe general or specific. As an example and not by way of limitation, if auser “likes” an article about a brand of shoes the category may be thebrand, or the general category of “shoes” or “clothing.” A connectionstore may be used for storing connection information about users. Theconnection information may indicate users who have similar or commonwork experience, group memberships, hobbies, educational history, or arein any way related or share common attributes. The connectioninformation may also include user-defined connections between differentusers and content (both internal and external). A web server may be usedfor linking the social-networking system 1060 to one or more clientsystems 1030 or one or more third-party systems 1070 via a network 1010.The web server may include a mail server or other messagingfunctionality for receiving and routing messages between thesocial-networking system 1060 and one or more client systems 1030. AnAPI-request server may allow a third-party system 1070 to accessinformation from the social-networking system 1060 by calling one ormore APIs. An action logger may be used to receive communications from aweb server about a user's actions on or off the social-networking system1060. In conjunction with the action log, a third-party-content-objectlog may be maintained of user exposures to third-party-content objects.A notification controller may provide information regarding contentobjects to a client system 1030. Information may be pushed to a clientsystem 1030 as notifications, or information may be pulled from a clientsystem 1030 responsive to a request received from a client system 1030.Authorization servers may be used to enforce one or more privacysettings of the users of the social-networking system 1060. A privacysetting of a user determines how particular information associated witha user can be shared. The authorization server may allow users to opt into or opt out of having their actions logged by the social-networkingsystem 1060 or shared with other systems (e.g., a third-party system1070), such as, for example, by setting appropriate privacy settings.Third-party-content-object stores may be used to store content objectsreceived from third parties, such as a third-party system 1070. Locationstores may be used for storing location information received from clientsystems 1030 associated with users. Advertisement-pricing modules maycombine social information, the current time, location information, orother suitable information to provide relevant advertisements, in theform of notifications, to a user.

FIG. 11 illustrates an example computer system 1100. In particularembodiments, one or more computer systems 1100 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1100 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1100 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1100.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1100. This disclosure contemplates computer system 1100 taking anysuitable physical form. As example and not by way of limitation,computer system 1100 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1100 may include one or more computersystems 1100; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1100 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1100 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1100 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1100 includes a processor1102, memory 1104, storage 1106, an input/output (I/O) interface 1108, acommunication interface 1110, and a bus 1112. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1102 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1102 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1104, or storage 1106; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1104, or storage 1106. In particularembodiments, processor 1102 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1102 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1102 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1104 or storage 1106, and the instruction caches may speed upretrieval of those instructions by processor 1102. Data in the datacaches may be copies of data in memory 1104 or storage 1106 forinstructions executing at processor 1102 to operate on; the results ofprevious instructions executed at processor 1102 for access bysubsequent instructions executing at processor 1102 or for writing tomemory 1104 or storage 1106; or other suitable data. The data caches mayspeed up read or write operations by processor 1102. The TLBs may speedup virtual-address translation for processor 1102. In particularembodiments, processor 1102 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1102 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1102 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1102. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1104 includes main memory for storinginstructions for processor 1102 to execute or data for processor 1102 tooperate on. As an example and not by way of limitation, computer system1100 may load instructions from storage 1106 or another source (such as,for example, another computer system 1100) to memory 1104. Processor1102 may then load the instructions from memory 1104 to an internalregister or internal cache. To execute the instructions, processor 1102may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1102 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1102 may then write one or more of those results to memory 1104. Inparticular embodiments, processor 1102 executes only instructions in oneor more internal registers or internal caches or in memory 1104 (asopposed to storage 1106 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1104 (asopposed to storage 1106 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1102 to memory 1104. Bus 1112 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1102 and memory 1104and facilitate accesses to memory 1104 requested by processor 1102. Inparticular embodiments, memory 1104 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1104 may include one ormore memories 1104, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1106 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1106 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1106 may include removable or non-removable (or fixed)media, where appropriate. Storage 1106 may be internal or external tocomputer system 1100, where appropriate. In particular embodiments,storage 1106 is non-volatile, solid-state memory. In particularembodiments, storage 1106 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1106taking any suitable physical form. Storage 1106 may include one or morestorage control units facilitating communication between processor 1102and storage 1106, where appropriate. Where appropriate, storage 1106 mayinclude one or more storages 1106. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1108 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1100 and one or more I/O devices. Computersystem 1100 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1100. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1108 for them. Where appropriate, I/Ointerface 1108 may include one or more device or software driversenabling processor 1102 to drive one or more of these I/O devices. I/Ointerface 1108 may include one or more I/O interfaces 1108, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1110 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1100 and one or more other computer systems 1100 or oneor more networks. As an example and not by way of limitation,communication interface 1110 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1110 for it. As an example and not by way oflimitation, computer system 1100 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1100 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1100 may include any suitable communicationinterface 1110 for any of these networks, where appropriate.Communication interface 1110 may include one or more communicationinterfaces 1110, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1112 includes hardware, software, or bothcoupling components of computer system 1100 to each other. As an exampleand not by way of limitation, bus 1112 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1112may include one or more buses 1112, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

1. A method comprising, by one or more computing systems: receiving animage of a portion of a face of a first user, wherein the image iscaptured by a camera coupled to an artificial-reality headset worn bythe first user; accessing a three-dimensional (3D) facial modelrepresentative of the face of the first user; identifying one or morefacial features captured in the image; determining a relative pose ofthe camera relative to the 3D facial model based on the identified oneor more facial features in the image and predetermined feature locationson the 3D facial model; determining a mapping relationship between theimage and the 3D facial model by projecting the image of the portion ofthe face of the first user onto the 3D facial model from the relativepose of the camera; and causing an output image of a facialrepresentation of the first user to be rendered using at least the 3Dfacial model and the mapping relationship between the image and the 3Dfacial model.
 2. The method of claim 1, further comprising: receiving aplurality of images corresponding to a second portion of the face of thefirst user, wherein the plurality of images is captured by a pluralityof cameras coupled to the artificial-reality headset worn by the firstuser; synthesizing, using a machine-learning model, to generate asynthesized image corresponding to the second portion of the face of thefirst user; and determining a second mapping relationship between thesynthesized image and the 3D facial model by projecting the synthesizedimage of the second portion of the face of the first user onto the 3Dfacial model from a predetermined camera pose.
 3. The method of claim 1,wherein the 3D facial model is a predetermined 3D facial modelrepresentative of a plurality of faces of a plurality of users.
 4. Themethod of claim 1, wherein the 3D facial model is generated by:morphing, based on at least the identified one or more facial featuresin the image, a predetermined 3D facial model representative of aplurality of faces of a plurality of users.
 5. The method of claim 1,wherein determining the relative pose of the camera comprises comparinglocations of the identified one or more facial features captured in theimage to the predetermined feature locations.
 6. The method of claim 1,wherein the output image of the facial representation of the first useris photorealistic.
 7. The method of claim 1, further comprising:receiving a second image of a second portion of the face of the firstuser, wherein the image is captured by a second camera coupled to theartificial-reality headset worn by the first user; identifying one ormore second facial features captured in the second image; determining asecond relative pose of the camera relative to the 3D facial model basedon the identified one or more second facial features in the second imageand the predetermined feature locations on the 3D facial model; anddetermining the mapping relationship between the second image and the 3Dfacial model by projecting the second image of the second portion of theface of the first user onto the 3D facial model from the second relativepose of the camera, wherein the output image further uses at least the3D facial model and the mapping relationship between the second imageand the 3D facial model.
 8. The method of claim 1, wherein the mappingrelationship is a texture image of the portion of the face of the firstuser, and wherein the texture image is blended with a predeterminedtexture corresponding to other portions of the face of the first user togenerate the output image for the facial representation of the firstuser.
 9. The method of claim 1, wherein the mapping relationship is atexture image of the portion of the face of the first user, and whereinthe rendering the facial representation of the first user comprises:sampling a first point on a predetermined texture corresponding to thefacial representation of the first user to identify a first colorassociated with the first point; sampling a second point on the textureimage that corresponds to the first point on the predetermined textureto identify a second color associated with the second point; andblending the first color of the first point with the second color of thesecond point to generate a final color associated with a locationcorresponding to the first point and the second point.
 10. The method ofclaim 1, wherein causing the output image of the facial representationof the first user to be rendered comprises: sending the 3D facial modeland the mapping relationship between the image and the 3D facial modelto a computing system associated with a second user; and rendering theoutput image based on a viewpoint of the second user with respect to thefirst user.
 11. One or more computer-readable non-transitory storagemedia embodying software that is operable when executed to: receive animage of a portion of a face of a first user, wherein the image iscaptured by a camera coupled to an artificial-reality headset worn bythe first user; access a three-dimensional (3D) facial modelrepresentative of the face of the first user; identify one or morefacial features captured in the image; determine a relative pose of thecamera relative to the 3D facial model based on the identified one ormore facial features in the image and predetermined feature locations onthe 3D facial model; determine a mapping relationship between the imageand the 3D facial model by projecting the image of the portion of theface of the first user onto the 3D facial model from the relative poseof the camera; and cause an output image of a facial representation ofthe first user to be rendered using at least the 3D facial model and themapping relationship between the image and the 3D facial model.
 12. Themedia of claim 11, wherein the software is further operable whenexecuted to: receive a plurality of images corresponding to a secondportion of the face of the first user, wherein the plurality of imagesis captured by a plurality of cameras coupled to the artificial-realityheadset worn by the first user; synthesize, using a machine-learningmodel, to generate a synthesized image corresponding to the secondportion of the face of the first user; and determine a second mappingrelationship between the synthesized image and the 3D facial model byprojecting the synthesized image of the second portion of the face ofthe first user onto the 3D facial model from a predetermined camerapose.
 13. The media of claim 11, wherein the 3D facial model is apredetermined 3D facial model representative of a plurality of faces ofa plurality of users.
 14. The media of claim 11, wherein the software isfurther operable when executed to: morph, based on at least theidentified one or more facial features in the image, a predetermined 3Dfacial model representative of a plurality of faces of a plurality ofusers.
 15. The media of claim 11, wherein determining the relative poseof the camera comprises comparing locations of the identified one ormore facial features captured in the image to the predetermined featurelocations.
 16. A system comprising: one or more processors; and one ormore computer-readable non-transitory storage media coupled to one ormore of the processors and comprising instructions operable whenexecuted by one or more of the processors to cause the system to:receive an image of a portion of a face of a first user, wherein theimage is captured by a camera coupled to an artificial-reality headsetworn by the first user; access a three-dimensional (3D) facial modelrepresentative of the face of the first user; identify one or morefacial features captured in the image; determine a relative pose of thecamera relative to the 3D facial model based on the identified one ormore facial features in the image and predetermined feature locations onthe 3D facial model; determine a mapping relationship between the imageand the 3D facial model by projecting the image of the portion of theface of the first user onto the 3D facial model from the relative poseof the camera; and cause an output image of a facial representation ofthe first user to be rendered using at least the 3D facial model and themapping relationship between the image and the 3D facial model.
 17. Thesystem of claim 16, wherein the processors are further operable whenexecuting the instructions to: receive a plurality of imagescorresponding to a second portion of the face of the first user, whereinthe plurality of images is captured by a plurality of cameras coupled tothe artificial-reality headset worn by the first user; synthesize, usinga machine-learning model, to generate a synthesized image correspondingto the second portion of the face of the first user; and determine asecond mapping relationship between the synthesized image and the 3Dfacial model by projecting the synthesized image of the second portionof the face of the first user onto the 3D facial model from apredetermined camera pose.
 18. The system of claim 16, wherein the 3Dfacial model is a predetermined 3D facial model representative of aplurality of faces of a plurality of users.
 19. The system of claim 16,wherein the processors are further operable when executing theinstructions to: morph, based on at least the identified one or morefacial features in the image, a predetermined 3D facial modelrepresentative of a plurality of faces of a plurality of users.
 20. Thesystem of claim 16, wherein determining the relative pose of the cameracomprises comparing locations of the identified one or more facialfeatures captured in the image to the predetermined feature locations.